Posts

This one slide sums up a decade (2006-2016) of evolution in the areas of data processing, analysis, computing, machine learning and artificial intelligence.

Clive Humby, UK Mathematician and architect of Tesco’s Clubcard, said in 2006 (widely credited as the first to coin the phrase): “Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

Like this:

If we remove the ability to have an emotional connect and being able to form meaningful bonds then we can as well hire AI bots to lead us, don’t really need a human leader. At the end I believe AI is going to make us more human by forcing us to focus on traits which define us as a human, something which no AI can manage (yet). #AI#Leadership

There is another old article from HBR which touches upon the subject of having robots as managers. It kinds of explores the subject with both pros and cons of having a robot as a manager.

In the end it simply depends on people – there are those who have seen abusive managers and leaders and are ready to give bots a chance to manage assuming it will still be better than what they have faced already while there are those who had an opportunity to work with really good humans as their managers and leaders who will never accept AI bots as their managers. Maybe, a time will come when everybody will have to make a choice between the two.

Like this:

Doesn’t matter how many times I go there and look at the open sky and the amazing view, it always looks new to me. For quite some time I have been thinking about capturing the sun set – had to wait for the perfect day with clear visibility, without any fog or haze to get these shots.

They have made an excellent case for a data driven innovation. Citing examples of how Google, Apple and Amazon are using data to further innovation and outsmarting not just their existing competitors but also the startups, they have made it very clear that the future innovation will be owned by enterprises which are able to leverage AI in generating insights from the data and not just rely purely on human ingenuity.

I am sharing here the two adjustments, they mentioned about, that enterprises need to make:

Now, one interesting point to ponder about is – what do we mean by data really ? In all of their examples it means the data generated by users which is being used by large corporations to generate insights for improving their products or services.

What does it mean for companies whose product or services are not consumed or delivered online ? eg. Pharmaceuticals

In that case they need to look at the data that is publicly available. Thankfully, there is a lot of data that is available publicly eg. information on clinical trials, research publications, disclosures to regulatory bodies, reviews from regulatory bodies, data from different scientific congresses across the world, theses from universities, patents from major patent bodies globally, data from major global regulatory bodies etc.

In the context of the second adjustment above, enterprises need to leverage AI to automate not just the collection and curation of data but also the generation of insights, specific to key processes, from the data. Entire Drug Development process is very long and complex, with a lot of tasks / processes which are repetitive in nature or involve a lot of manual content reviews at each step. It is up to the enterprises to empower their teams to step up the value chain from doing manual analysis of data to being the domain specialists who can partner with Data Scientists to come up with good training data sets, to validate the output of these algorithms and to be the data quality supervisors.

In the end, it will only improve the overall satisfaction of employees as they will free to solve real problems instead of clerical tasks, which ultimately leads to happier and more productive workplace.

P.S. The title (Innovation eats data for breakfast) is just a pun on the famous “Culture eats strategy for breakfast”, a phrase coined by the legendary Peter Drucker.

Like this:

The goal for any content driven website is to provide its users with relevant content based on Information Discovery. Most of the websites choose ‘search’ as a tool for information discovery (Netflix is a mind blowing exception to this) and in search, relevance is heavily dependent on context rather than just strings present in them. Not many websites realise this and I was surprised to see that HBR.org , known for having one of the best content repositories, giving barely useful results for my search queries.

Now, I am a happy subscriber of Harvard Business Review and have been using it often to enhance my limited knowledge by discovering interesting content around technology, business, leadership etc. and have absolutely no doubt on the high quality of its content and really great contributors. But, I am disappointed at how that content is becoming difficult to discover in the first place. Here is how its search looks like:

:

All looks well, it has a very familiar interface, like most of the other sites have and the first result does have the word I searched for “search”. Don’t go away yet, stay with me till the end 🙂

In my quest to learn more and follow everything about Artificial Intelligence I keep looking everywhere for anything related to AI. I thought let’s try what all is there in HBR.org repository on AI. I search first for artificial intelligence and look at the results:

First result is a “Sales and Marketing” case study about an early stage company Empathetics (an organization that teaches empathy to healthcare professionals and staff to improve the patient experience) and I wonder why is that the top result (when sorted by relevance!) for what I searched for. I open it, scratch my head really hard to figure out what exactly is related to AI there but could not find anything. I scroll down and to my despair, the other results are also out of the world for me. Here they are:

and

Then I thought of trying the ‘exactness’ trick – I searched for “Artificial Intelligence” and boom!

ZERO results! Apparently it does not support exact string search, which ideally it should. I knew this is simply not true as HBR.org does have articles on AI. Examples:

It gives irrelevant results in search even though the right content is very much there in repository.

Moving on, I tried taking my chances on AI – I search for ‘AI’. Here is what happens:

Do you notice it? There is an author Ai-Ling Jamila Malone whose name contains “AI” and HBR.org simply is showing me all articles from the author. This means it is giving a higher score (probably) towords found in a wrong field (author field) than the content itself and that too without any context.

Now it could have been deliberately done assuming most of the people want to search for names of authors but hey, that use case CAN be handled in a better way.

Moving further, I check for another hot topic – Deep Learning – and default (sorted by relevance) results appear to be relevant (see now it shows me results for artificial intelligence as well).

But the articles are old and I needed the latest ones – the moment I try sorting by publication date I see this:

the first result is this – https://hbr.org/2018/01/the-5-things-your-ai-unit-needs-to-do – which may seem somewhat relevant given it has AI in its title but it is not even remotely related to “Deep Learning” and the only reason it appears in search results is because there is a word “deep” in one paragraph somewhere

they have the words “deep” and “learning” somewhere and hence it is being shown. An important point to consider – I want latest but still relevant results and HBR.org fails at it. It does not identify ‘deep learning’ as a concept made of two words and is simply looking up the words appearing somewhere in the content.

The root causes for all of it can be summarised as follows:

The search at HBR.org is still relying on basic keyword based scoring and has no Ontology of concepts like “Artificial Intelligence” or “Deep Learning” or the relationships between the concepts.

It does not account for synonyms and hence is unable to understand that “Artificial Intelligence” and “AI” are same concepts. An Ontology makes it much easier to maintain all synonyms of any given concept.

It does not identify entities so is unable to differentiate between name of a person “Ai-Ling Jamila Malone” and a concept “AI”

Based on how we have designed information discovery for our Data as a Service (DaaS) platform iPlexus.ai I can say that the primary reason for all of the problems is the missing Ontology leading to missing Entity Recognition and disambiguation.

Search is important but in itself is not always the best way for information discovery. Users don’t get happy at seeing millions of search results for what they search – that only adds to information overload. What matters is, if you are telling me there are so many possible results out there, then tell me how they are distributed across different dimensions around my ‘interest’. Let me choose a direction and don’t force me to keep going through all the results in a linear fashion – nobody will live through to get to the end of the millionth result page. And not just Innoplexus but I know there are few other companies out there who are following this philosophy and making Information Discovery easier for their users. One of the examples I quoted above as well is Netflix.

True information discovery tool has to be a Digital Gyroscope helping one to explore the Data Universe by giving a sense of all possible directions and saving one from getting lost in the hyperspace.

Thanks to Ravi Ranjan for reviewing the article and helping with the headline 🙂

Like this:

Slow clap for all of those who thought Bitcoin (or other crypto currencies) were / are not being manipulated. Techcrunch reported on 15th Jan and MIT Technology Review reported on 16th Jan about a research which gives insights into how Bitcoin was being manipulated by two bots named Markus and Willy – in 2013. They performed valid trades but did not actually own any bitcoin themselves.

Entitled “Price Manipulation in the Bitcoin Ecosystem” and appearing in the recent issue of the Journal of Monetary Economics the paper describes to what degree the Bitcoin ecosystem is controlled by bad actors. Based on rigorous analysis with extensive robustness checks, the paper demonstrates that the suspicious trading activity likely caused the unprecedented spike in the USD-BTC exchange rate in late 2013, when the rate jumped from around $150 to more than $1,000 in two months.”

Now you can imagine if this happened in 2013 then what is happening today. For all its talk about decentralisation, the fact that it is so susceptible to such manipulations, should be a warning for everybody trying to ‘try their luck’ in Bitcoin or other crypto currencies.

Like this:

If your culture doesn’t like geeks, you are in real trouble. @Bill Gates said that in 2010. (Not) surprisingly, most of the companies still have not understood it. Look at the world’s most valuable companies – they know that for sure and made a strategy around that only.

Like this:

I came across this article on HBR today which looks at the world’s best restaurants to find out how they balance Innovation and Consistency.

Here’s the summary :

Despite being able to charge hundreds of dollars for a meal and being fully booked months in advance, top restaurants often still have a hard time turning a profit. And they face an added challenge of maintaining flawless consistency, while simultaneously being innovative and cutting-edge. This requires dedicated time and space for research and experimentation, as well as a thorough process for both iterating on and standardizing new inventions. Examples of restaurants that have made both the Michelin Guide and 50 Best Restaurants of the World list show how they encourage creativity and learning beyond the leadership or lab teams, and generate, refine, and standardize ideas.

All the projects follow a specific development process, alternating between collective ideation or feedback and focused work by a small team. For restaurant dishes, the development team will quickly prototype and iterate through numerous versions of the dish and its components, either in the lab or if a lab is not available, in the main kitchen during slow hours. The trials can go over for months as numerous variations are tested in a race against seasonal ingredients.

This just goes on to prove that Consistency and Creativity are not mutually exclusive – restaurants need to be both at the same time. Innovation which is not a result of a set process will soon become unsustainable – one may be able to innovate even without process at times but no one can repeat the feat a number of times. That consistency is possible only with a process.